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000165528 1001_ $$0P:(DE-2719)2811327$$aFaber, Jennifer$$b0$$eFirst author$$udzne
000165528 245__ $$aCerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation.
000165528 260__ $$aOrlando, Fla.$$bAcademic Press$$c2022
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000165528 520__ $$aQuantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer).
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000165528 650_7 $$2Other$$aCerebNet
000165528 650_7 $$2Other$$aCerebellum
000165528 650_7 $$2Other$$aComputational neuroimaging
000165528 650_7 $$2Other$$aDeep learning
000165528 650_2 $$2MeSH$$aHumans
000165528 650_2 $$2MeSH$$aImage Processing, Computer-Assisted: methods
000165528 650_2 $$2MeSH$$aDeep Learning
000165528 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000165528 650_2 $$2MeSH$$aReproducibility of Results
000165528 650_2 $$2MeSH$$aCerebellum: diagnostic imaging
000165528 7001_ $$0P:(DE-2719)2814290$$aKügler, David$$b1$$udzne
000165528 7001_ $$aBahrami, Emad$$b2
000165528 7001_ $$0P:(DE-2719)2814092$$aHeinz, Lea-Sophie$$b3$$udzne
000165528 7001_ $$aTimmann, Dagmar$$b4
000165528 7001_ $$aErnst, Thomas M$$b5
000165528 7001_ $$0P:(DE-2719)9001745$$aDeike-Hofmann, Katerina$$b6$$udzne
000165528 7001_ $$0P:(DE-2719)2810314$$aKlockgether, Thomas$$b7$$udzne
000165528 7001_ $$avan de Warrenburg, Bart$$b8
000165528 7001_ $$avan Gaalen, Judith$$b9
000165528 7001_ $$aReetz, Kathrin$$b10
000165528 7001_ $$aRomanzetti, Sandro$$b11
000165528 7001_ $$aOz, Gulin$$b12
000165528 7001_ $$aJoers, James M$$b13
000165528 7001_ $$aDiedrichsen, Jorn$$b14
000165528 7001_ $$aGroup, ESMI MRI Study$$b15$$eCollaboration Author
000165528 7001_ $$0P:(DE-2719)2812134$$aReuter, Martin$$b16$$udzne
000165528 7001_ $$aGiunti, Paola$$b17
000165528 7001_ $$aGarcia-Moreno, Hector$$b18
000165528 7001_ $$0P:(DE-2719)2811564$$aJacobi, Heike$$b19$$udzne
000165528 7001_ $$aJende, Johann$$b20
000165528 7001_ $$ade Vries, Jeroen$$b21
000165528 7001_ $$aPovazan, Michal$$b22
000165528 7001_ $$aBarker, Peter B$$b23
000165528 7001_ $$aSteiner, Katherina Marie$$b24
000165528 7001_ $$aKrahe, Janna$$b25
000165528 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2022.119703$$gVol. 264, p. 119703 -$$p119703$$tNeuroImage$$v264$$x1053-8119$$y2022
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